Time-dependent fuzzy random location-scheduling programming for hazardous materials transportation

•We focus on location-scheduling programming for hazardous materials transportation.•Fuzzy-randomness is incorporated into the expected value location-scheduling models.•Practical factors such as time-dependent risks and road conditions are considered.•Greedy method based adaptive hybrid particle swarm optimization algorithm is used.•Numerical cases show the feasibility and optimality of the models and algorithms.

The tremendous use of hazardous materials has promoted the economic development, which also brings about a growing risk causing a widespread concern. In this work, we consider a location-scheduling problem on hazardous materials transportation under the assumption that transportation risks are time-dependent fuzzy random variables. First, we formulate a scheduling optimization model and design a fuzzy random simulation based genetic algorithm to optimize the departure time and dwell times for each depot–customer pair. Then we establish an expected value model and design a modified particle swarm optimization algorithm to minimize the en route risks and site risks. Finally, numerical examples are given to illustrate the effectiveness of the proposed models and algorithms.